


import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE     :					List[str]      =       logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE     :					Dict      =       {
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}
def      UpperCAmelCase__		(UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_							):



     """simple docstring"""






     for attribute in key.split('''.'''							):
          snake_case											=							getattr(UpperCamelCase_				,UpperCamelCase_							)

     if weight_type is not None:
          snake_case											=							getattr(UpperCamelCase_				,UpperCamelCase_							).shape
     else:
          snake_case											=							hf_pointer.shape

     assert hf_shape == value.shape, (
         F'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
         F''' {value.shape} for {full_name}'''
     )

     if weight_type == "weight":
          snake_case											=							value
     elif weight_type == "weight_g":
          snake_case											=							value
     elif weight_type == "weight_v":
          snake_case											=							value
     elif weight_type == "bias":
          snake_case											=							value
     else:
          snake_case											=							value

     logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''							)
def      UpperCAmelCase__		(UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_							):



     """simple docstring"""






     snake_case											=							[]
     snake_case											=							fairseq_model.state_dict()

     snake_case											=							hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

     for name, value in fairseq_dict.items():
          snake_case											=							False
          if "conv_layers" in name:
               load_conv_layer(
                   UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_				,hf_model.config.feat_extract_norm == '''group'''				,)
               snake_case											=							True
          else:
               for key, mapped_key in MAPPING.items():
                    snake_case											=							'''hubert.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key

                    if key in name or (key.split('''w2v_model.'''							)[-1] == name.split('''.'''							)[0] and not is_finetuned):
                         snake_case											=							True
                         if "*" in mapped_key:
                              snake_case											=							name.split(UpperCamelCase_							)[0].split('''.'''							)[-2]
                              snake_case											=							mapped_key.replace('''*'''				,UpperCamelCase_							)
                         if "weight_g" in name:
                              snake_case											=							'''weight_g'''
                         elif "weight_v" in name:
                              snake_case											=							'''weight_v'''
                         elif "weight" in name:
                              snake_case											=							'''weight'''
                         elif "bias" in name:
                              snake_case											=							'''bias'''
                         else:
                              snake_case											=							None
                         set_recursively(UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_							)
                    continue
          if not is_used:
               unused_weights.append(UpperCamelCase_							)

     logger.warning(F'''Unused weights: {unused_weights}'''							)
def      UpperCAmelCase__		(UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_							):



     """simple docstring"""






     snake_case											=							full_name.split('''conv_layers.'''							)[-1]
     snake_case											=							name.split('''.'''							)
     snake_case											=							int(items[0]							)
     snake_case											=							int(items[1]							)

     if type_id == 0:
          if "bias" in name:
               assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                   F'''{full_name} has size {value.shape}, but'''
                   F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
               )
               snake_case											=							value
               logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''							)
          elif "weight" in name:
               assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                   F'''{full_name} has size {value.shape}, but'''
                   F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
               )
               snake_case											=							value
               logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''							)
     elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
          if "bias" in name:
               assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                   F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
                   " found."
               )
               snake_case											=							value
               logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''							)
          elif "weight" in name:
               assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                   F'''{full_name} has size {value.shape}, but'''
                   F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
               )
               snake_case											=							value
               logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''							)
     else:
          unused_weights.append(UpperCamelCase_							)
@torch.no_grad()
def      UpperCAmelCase__		(UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_=None				,UpperCamelCase_=None				,UpperCamelCase_=True							):



     """simple docstring"""






     if config_path is not None:
          snake_case											=							HubertConfig.from_pretrained(UpperCamelCase_							)
     else:
          snake_case											=							HubertConfig()

     if is_finetuned:
          if dict_path:
               snake_case											=							Dictionary.load(UpperCamelCase_							)

               # important change bos & pad token id since CTC symbol is <pad> and
               # not <s> as in fairseq
               snake_case											=							target_dict.pad_index
               snake_case											=							target_dict.bos_index
               snake_case											=							target_dict.eos_index
               snake_case											=							len(target_dict.symbols							)
               snake_case											=							os.path.join(UpperCamelCase_				,'''vocab.json'''							)
               if not os.path.isdir(UpperCamelCase_							):
                    logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(UpperCamelCase_							)							)
                    return
               os.makedirs(UpperCamelCase_				,exist_ok=UpperCamelCase_							)
               with open(UpperCamelCase_				,'''w'''				,encoding='''utf-8'''							) as vocab_handle:
                    json.dump(target_dict.indices				,UpperCamelCase_							)
               snake_case											=							WavaVecaCTCTokenizer(
                   UpperCamelCase_				,unk_token=target_dict.unk_word				,pad_token=target_dict.pad_word				,bos_token=target_dict.bos_word				,eos_token=target_dict.eos_word				,word_delimiter_token='''|'''				,do_lower_case=UpperCamelCase_				,)
               snake_case											=							True if config.feat_extract_norm == '''layer''' else False
               snake_case											=							WavaVecaFeatureExtractor(
                   feature_size=1				,sampling_rate=1_60_00				,padding_value=0				,do_normalize=UpperCamelCase_				,return_attention_mask=UpperCamelCase_				,)
               snake_case											=							WavaVecaProcessor(feature_extractor=UpperCamelCase_				,tokenizer=UpperCamelCase_							)
               processor.save_pretrained(UpperCamelCase_							)

          snake_case											=							HubertForCTC(UpperCamelCase_							)
     else:
          snake_case											=							HubertModel(UpperCamelCase_							)

     if is_finetuned:
          snake_case					,    snake_case					,    snake_case											=							fairseq.checkpoint_utils.load_model_ensemble_and_task(
              [checkpoint_path]				,arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''							)[:-1]							)}							)
     else:
          snake_case					,    snake_case					,    snake_case											=							fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]							)

     snake_case											=							model[0].eval()

     recursively_load_weights(UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_							)

     hf_wavavec.save_pretrained(UpperCamelCase_							)


if __name__ == "__main__":
 _SCREAMING_SNAKE_CASE     :					Tuple      =       argparse.ArgumentParser()
 parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
 parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
 parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
 parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
 parser.add_argument(
     "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
 )
 _SCREAMING_SNAKE_CASE     :					Dict      =       parser.parse_args()
 convert_hubert_checkpoint(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
 )








import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline
def      UpperCAmelCase__		(UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_							):



     """simple docstring"""






     snake_case											=							StableDiffusionPipeline.from_pretrained(UpperCamelCase_				,torch_dtype=torch.floataa							)

     # load LoRA weight from .safetensors
     snake_case											=							load_file(UpperCamelCase_							)

     snake_case											=							[]

     # directly update weight in diffusers model
     for key in state_dict:
          # it is suggested to print out the key, it usually will be something like below
          # "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"

          # as we have set the alpha beforehand, so just skip
          if ".alpha" in key or key in visited:
               continue

          if "text" in key:
               snake_case											=							key.split('''.'''							)[0].split(LORA_PREFIX_TEXT_ENCODER + '''_'''							)[-1].split('''_'''							)
               snake_case											=							pipeline.text_encoder
          else:
               snake_case											=							key.split('''.'''							)[0].split(LORA_PREFIX_UNET + '''_'''							)[-1].split('''_'''							)
               snake_case											=							pipeline.unet

          # find the target layer
          snake_case											=							layer_infos.pop(0							)
          while len(UpperCamelCase_							) > -1:
               try:
                    snake_case											=							curr_layer.__getattr__(UpperCamelCase_							)
                    if len(UpperCamelCase_							) > 0:
                         snake_case											=							layer_infos.pop(0							)
                    elif len(UpperCamelCase_							) == 0:
                         break
               except Exception:
                    if len(UpperCamelCase_							) > 0:
                         temp_name += "_" + layer_infos.pop(0							)
                    else:
                         snake_case											=							layer_infos.pop(0							)

          snake_case											=							[]
          if "lora_down" in key:
               pair_keys.append(key.replace('''lora_down'''				,'''lora_up'''							)							)
               pair_keys.append(UpperCamelCase_							)
          else:
               pair_keys.append(UpperCamelCase_							)
               pair_keys.append(key.replace('''lora_up'''				,'''lora_down'''							)							)

          # update weight
          if len(state_dict[pair_keys[0]].shape							) == 4:
               snake_case											=							state_dict[pair_keys[0]].squeeze(3							).squeeze(2							).to(torch.floataa							)
               snake_case											=							state_dict[pair_keys[1]].squeeze(3							).squeeze(2							).to(torch.floataa							)
               curr_layer.weight.data += alpha * torch.mm(UpperCamelCase_				,UpperCamelCase_							).unsqueeze(2							).unsqueeze(3							)
          else:
               snake_case											=							state_dict[pair_keys[0]].to(torch.floataa							)
               snake_case											=							state_dict[pair_keys[1]].to(torch.floataa							)
               curr_layer.weight.data += alpha * torch.mm(UpperCamelCase_				,UpperCamelCase_							)

          # update visited list
          for item in pair_keys:
               visited.append(UpperCamelCase_							)

     return pipeline


if __name__ == "__main__":
 _SCREAMING_SNAKE_CASE     :					Dict      =       argparse.ArgumentParser()

 parser.add_argument(
     "--base_model_path", default=None, type=str, required=True, help="Path to the base model in diffusers format."
 )
 parser.add_argument(
     "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
 )
 parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
 parser.add_argument(
     "--lora_prefix_unet", default="lora_unet", type=str, help="The prefix of UNet weight in safetensors"
 )
 parser.add_argument(
     "--lora_prefix_text_encoder",
     default="lora_te",
     type=str,
     help="The prefix of text encoder weight in safetensors",
 )
 parser.add_argument("--alpha", default=0.75, type=float, help="The merging ratio in W = W0 + alpha * deltaW")
 parser.add_argument(
     "--to_safetensors", action="store_true", help="Whether to store pipeline in safetensors format or not."
 )
 parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)")

 _SCREAMING_SNAKE_CASE     :					List[Any]      =       parser.parse_args()

 _SCREAMING_SNAKE_CASE     :					Optional[int]      =       args.base_model_path
 _SCREAMING_SNAKE_CASE     :					Dict      =       args.checkpoint_path
 _SCREAMING_SNAKE_CASE     :					List[str]      =       args.dump_path
 _SCREAMING_SNAKE_CASE     :					Dict      =       args.lora_prefix_unet
 _SCREAMING_SNAKE_CASE     :					List[str]      =       args.lora_prefix_text_encoder
 _SCREAMING_SNAKE_CASE     :					Optional[Any]      =       args.alpha

 _SCREAMING_SNAKE_CASE     :					Optional[int]      =       convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

 _SCREAMING_SNAKE_CASE     :					Tuple      =       pipe.to(args.device)
 pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)




